Recent advances in microarray technologies have resulted in a flood of genomics data. This large body of accumulated data could be used as a knowledge base to help researchers interpret new experimental data. ArraySearch finds statistical correlations between newly observed gene expression profiles and the huge source of well-characterized expression signatures deposited in the public domain. A search query of a list of genes will return experiments on which the genes are significantly up- or downregulated collectively. Searches can also be conducted using gene expression signatures from new experiments. This resource will empower biological researchers with a statistical method to explore expression data from their own research by comparing it with expression signatures from a large public archive. 1. Introduction In recent years, there has been a massive influx of data from DNA microarray experiments into publicly available repositories such as the National Center for Biotechnology Information (NCBI). Many of these datasets represent genomewide expression profiles generated from experiments investigating controlled perturbations on molecular pathways, responses to chemical stimuli, responses to pathogen infections, or mutant behavior when certain genes are made inoperative. The largest repository of gene expression data is NCBI’s Gene Expression Omnibus (GEO). As of July 1, 2011, GEO contains the expression profiles of 587,214 biological samples from different organisms. It is important to take advantage of this large body of data for the purpose of new genomics studies. A number of sites and tools have become available that attempt to utilize the data in ways that will guide future research. Although GEO is primarily designed for information storage and downloading, GEO profiles provide methods for querying the expression profiles of genes (see http://www.ncbi.nlm.nih.gov/geoprofiles). The Nottingham Arabidopsis Stock Center microarray service (NASCArrays) [1] also provides a number of useful data mining tools for exploring a large collection of gene expression data. The Two Gene Scatter Plot tool allows a user to visualize gene expression over all microarray slides for two genes as a scatter plot. The Spot History tool allows a user to see patterns of gene expression over all slides for specific genes. Genevestigator [2] provides online tools to access large and well-annotated datasets of curated microarray samples. The meta-profile analysis function of Genevestigator summarizes gene expression data across many samples according to the biological
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